Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT

Chen, X; Zhou, Bo; Xie, Huidong; Shi, Luyao; Liu, Hui; Holler, Wolfgang; Lin, MingDe; Liu, Yi-Hwa; Miller, Edward J.; Sinusas, Albert J.; Liu, Chi

doi:10.1007/s00259-022-05718-8

Cited by 33 publications

(20 citation statements)

References 41 publications

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“…Chen et al [ 52 ] compared the efficiency between direct and indirect techniques for dedicated SPECT and general purpose SPECT datasets by developing U-Net and DuRDN. In both approaches, AC was performed using CT-derived μ-maps as ground truth.…”

Section: Resultsmentioning

confidence: 99%

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

et al. 2023

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Deep learning (DL) has a growing popularity and is a well-established method of artificial intelligence for data processing, especially for images and videos. Its applications in nuclear medicine are broad and include, among others, disease classification, image reconstruction, and image de-noising. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are major image acquisition technologies in nuclear medicine. Though several studies have been conducted to apply DL in many nuclear medicine domains, such as cancer detection and classification, few studies have employed such methods for cardiovascular disease applications. The present paper reviews recent DL approaches focused on cardiac SPECT imaging. Extensive research identified fifty-five related studies, which are discussed. The review distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction. In addition, major findings and dominant techniques employed for the mentioned task are revealed. Current limitations of DL approaches and future research directions are discussed.

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Section: Resultsmentioning

confidence: 99%

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

et al. 2023

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“…Although direct attenuation/scatter correction in the image domain has a number of advantages, the generation of pseudo µ-maps (synthetic CT) from non-attenuation corrected images or MR images would provide an explainable AC map to verify/detect errors/drawbacks within PET attenuation and scatter correction procedures [ 20 , 66 , 72 , 73 ]. The suboptimal performance of direct AC approaches cannot be easily depicted from the resulting PET-AC images (local under/over estimation of radiotracer uptake).…”

Section: Discussionmentioning

confidence: 99%

Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

Shiri

Sadr

Akhavan

et al. 2022

Eur J Nucl Med Mol Imaging

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Purpose Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images. Methods Non-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center). Results In terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21–14.81%) and FL-PL (CI:11.82–13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32–12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34–26.10%). Furthermore, the Mann–Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R2 = 0.94), FL-SQ (R2 = 0.93), and FL-PL (R2 = 0.92), while CB model achieved a far lower coefficient of determination (R2 = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed. Conclusion Deep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers.

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“…A group of randomly generated transformation parameters (∆t x , ∆t y , ∆t z , ∆α x , ∆α y , ∆α z ) were used to transform µ-maps to simulate misregistration. Ranges of translations (∆t x , ∆t y , ∆t z ) and rotations (∆α x , ∆α y , ∆α z ) were limited to (8,8,4) voxels and (10, 10, 30) degrees with the interval of 0.01 voxels and 0.01 degrees. Wider ranges were assigned for ∆t x , ∆t y , and ∆α z since misregistration of SPECT and CT mainly exists within the transverse plane.…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

“…In this study, we propose a novel Dual-Branch Squeeze-Fusion-Excitation (DuSFE) attention module for feature fusion, recalibration, and registration of cardiac SPECT and CT-derived µ-maps, inspired by Squeeze-and-Excitation Networks (SENet) [10]. SENet was developed to recalibrate channel weights of feature maps, which was applied for image fusion [12], segmentation [18], and transformation [3,4]. In our study, the DuSFE module fuses the information of multiple modalities and then recalibrates both the channel-wise and spatial features of each modality in a dual-branch manner.…”

Section: Introductionmentioning

confidence: 99%

Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT

Chen¹,

Zhang²,

Xie³

et al. 2022

Preprint

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Single-photon emission computed tomography (SPECT) is a widely applied imaging approach for diagnosis of coronary artery diseases. Attenuation maps (µ-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve diagnostic accuracy of cardiac SPECT. However, SPECT and CT are obtained sequentially in clinical practice, which potentially induces misregistration between the two scans. Convolutional neural networks (CNN) are powerful tools for medical image registration. Previous CNN-based methods for cross-modality registration either directly concatenated two input modalities as an early feature fusion or extracted image features using two separate CNN modules for a late fusion. These methods do not fully extract or fuse the cross-modality information. Besides, deep-learningbased rigid registration of cardiac SPECT and CT-derived µ-maps has not been investigated before. In this paper, we propose a Dual-Branch Squeeze-Fusion-Excitation (DuSFE) module for the registration of cardiac SPECT and CT-derived µ-maps. DuSFE fuses the knowledge from multiple modalities to recalibrate both channel-wise and spatial features for each modality. DuSFE can be embedded at multiple convolutional layers to enable feature fusion at different spatial dimensions. Our studies using clinical data demonstrated that a network embedded with DuSFE generated substantial lower registration errors and therefore more accurate AC SPECT images than previous methods. Our source code is available at https://github.com/XiongchaoChen/DuSFE_CrossRegistration.

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Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT

Cited by 33 publications

References 41 publications

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT

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